Statistical inference for periodic and partially observable poisson processes

This thesis develops practical Bayesian estimators and exploration methods for count data collected by autonomous robots with unreliable sensors for long periods of time. It addresses the problems of drawing inferences from temporally incomplete and unreliable count data. This thesis contributes statistical models with spectral analysis which are able to capture the periodic structure of count data on extended temporal scales from temporally sparse observations. It is shown how to use these patterns to i) predict the human activity level at particular times and places and ii) categorize locations based on their periodic patterns. The second main contribution is a set of inference methods for a Poisson process which takes into account the unreliability of the detection algorithms used to count events. Two tractable approximations to the posterior of such Poisson processes are presented to cope with the absence of a conjugate density. Variations of these processes are presented, in which (i) sensors are uncorrelated, (ii) sensors are correlated, (iii) the unreliability of the observation model, when built from data, is accounted for. A simulation study shows that these partially observable Poisson process (POPP) filters correct the over- and under-counts produced by sensors. The third main contribution is a set of exploration methods which brings together the spectral models and the POPP filters to drive exploration by a mobile robot for a series of nine-week deployments. This leads to (i) a labelled data set and (ii) solving an exploration exploitation trade-off: the robot must explore to find out where activities congregate, so as to then exploit that by observing as many activities

Real-time multisensor people tracking for human-robot spatial interaction

All currently used mobile robot platforms are able to navigate safely through their environment, avoiding static and dynamic obstacles. However, in human populated environments mere obstacle avoidance is not sufficient to make humans feel comfortable and safe around robots. To this end, a large community is currently producing human-aware navigation approaches to create a more socially acceptable robot behaviour. Amajorbuilding block for all Human-Robot Spatial Interaction is the ability of detecting and tracking humans in the vicinity of the robot. We present a fully integrated people perception framework, designed to run in real-time on a mobile robot. This framework employs detectors based on laser and RGB-D data and a tracking approach able to fuse multiple detectors using different versions of data association and Kalman filtering. The resulting trajectories are transformed into Qualitative Spatial Relations based on a Qualitative Trajectory Calculus, to learn and classify different encounters using a Hidden Markov Model based representation. We present this perception pipeline, which is fully implemented into the Robot Operating System (ROS), in a small proof of concept experiment. All components are readily available for download, and free to use under the MIT license, to researchers in all fields, especially focussing on social interaction learning by providing different kinds of output, i.e. Qualitative Relations and trajectories

Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers

17 pages, 1 figure, 3 tablesPreprin

Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting

Publisher PD

PENGENALAN DAN PENCARIAN POSISI ROBOT DALAM PENCARIAN SUMBER KEBOCORAN GAS

Implementasi algoritma Particle Swarm Optimization (PSO) pada robot untuk pencarian sumber kebocoran gas memerlukan informasi posisi dari setiap robot. Penggunaan kamera memungkinkan untuk mendapatkan informasi posisi robot yang lebih absolut dan akurat dibandingkan dengan perangkat pencari informasi posisi lainnya. Paper ini memaparkan suatu metode yang akan digunakan untuk mencari posisi robot. Metode yang diajukan menggunakan beberapa teknik pengolahan citra, seperti, Color Filtering dan Blobs Filtering guna mengenali dan memperoleh posisi robot. Berdasarkan eksperimen yang telah dilakukan, metode ini mampu menentukan posisi absolut robot sehingga ia dapat menyediakan informasi posisi robot dalam implementasi algoritma PSO. Implementation of Particle Swarm Optimization (PSO) algorithm on the robot to search for the gas leakage source requires information of the position of each robot. The use of the camera allows the robot to obtain position information more accurate than the absolute position information of other search tools. This paper describes a method that will be used to locate the position of the robot. Proposed method uses several image processing techniques, such as, Color Filtering and Blobs Filtering in order to identify and obtain the robot position. Based on the experiments that have been performed, the method is capable of determining the absolute position of the robot so that it can provide position information of the robot in PSO algorithm implementation

A Poisson-spectral model for modelling temporal patterns in human data observed by a robot

The efficiency of autonomous robots depends on how well they understand their operating environment. While most of the traditional environment models focus on the spatial representation, long-term mobile robot operation in human populated environments requires that the robots have a basic model of human behaviour. We present a framework that allows us to retrieve and represent aggregate human behaviour in large, populated environments on extended temporal scales. Our approach, based on time-varying Poisson process models and spectral analysis, efficiently retrieves long-term, re-occurring patterns of human activity from robot-gathered observations and uses these patterns to i) predict human activity level at particular times and places and ii) classify locations based on their periodic patterns of activity. The application of our framework on real-world data, gathered by a mobile robot operating in an indoor environment for one month, indicates that its predictive capabilities outperform other temporal modelling methods while being computationally more efficient. The experiment also demonstrates that spectral signatures act as features that allow us to classify room types which semantically match with humans' expectations

A multi-robot platform for the autonomous operation and maintenance of offshore wind farms

With the increasing scale of offshore wind farm development, maintaining farms efficiently and safely becomes a necessity. The length of turbine downtime and the logistics for human technician transfer make up a significant proportion of the operation and maintenance (O&M) costs. To reduce such costs, future O&M infrastructures will increasingly rely on offshore autonomous robotic solutions that are capable of co-managing wind farms with human operators located onshore. In particular, unmanned aerial vehicles, autonomous surface vessels and crawling robots are expected to play important roles not only to bring down costs but also to significantly reduce the health and safety risks by assisting (or replacing) human operators in performing the most hazardous tasks. This paper portrays a visionary view in which heterogeneous robotic assets, underpinned by AI agent technology, coordinate their behavior to autonomously inspect, maintain and repair offshore wind farms over long periods of time and unstable weather conditions. They cooperate with onshore human operators, who supervise the mission at a distance, via the use of shared deliberation techniques. We highlight several challenging research directions in this context and offer ambitious ideas to tackle them as well as initial solutions

A multimodal dataset of real world mobility activities in Parkinson’s disease

Parkinson’s disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson’s disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being “on” or “off” medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated